A Meta-Classification Model for Optimized ZBot Malware Prediction Using Learning Algorithms

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F23%3A10252669" target="_blank" >RIV/61989100:27230/23:10252669 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2227-7390/11/13/2840" target="_blank" >https://www.mdpi.com/2227-7390/11/13/2840</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/math11132840" target="_blank" >10.3390/math11132840</a>

Result language

angličtina

Original language name

A Meta-Classification Model for Optimized ZBot Malware Prediction Using Learning Algorithms

Original language description

Botnets pose a real threat to cybersecurity by facilitating criminal activities like malware distribution, attacks involving distributed denial of service, fraud, click fraud, phishing, and theft identification. The methods currently used for botnet detection are only appropriate for specific botnet commands and control protocols; they do not endorse botnet identification in early phases. Security guards have used honeypots successfully in several computer security defence systems. Honeypots are frequently utilised in botnet defence because they can draw botnet compromises, reveal spies in botnet membership, and deter attacker behaviour. Attackers who build and maintain botnets must devise ways to avoid honeypot traps. Machine learning methods support identification and inhibit bot threats to address the problems associated with botnet attacks. To choose the best features to feed as input to the machine learning classifiers to estimate the performance of botnet detection, a Kernel-based Ensemble Meta Classifier (KEMC) Strategy is suggested in this work. And particle swarm optimization (PSO) and genetic algorithm (GA) intelligent optimization algorithms are used to establish the ideal order. The model covered in this paper is employed to forecast Internet cyber security circumstances. The Binary Cross-Entropy (loss), the GA-PSO optimizer, the Softsign activation functions and ensembles were used in the experiment to produce the best results. The model succeeded because Forfileless malware, gathered from well-known datasets, achieved a total accuracy of 93.3% with a True Positive (TP) Range of 87.45% at zero False Positive (FP). (C) 2023 by the authors.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20301 - Mechanical engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Mathematics

ISSN

2227-7390

e-ISSN

—

Volume of the periodical

11

Issue of the periodical within the volume

13

Country of publishing house

CH - SWITZERLAND

Number of pages

21

Pages from-to

—

UT code for WoS article

001028282600001

EID of the result in the Scopus database

2-s2.0-85164926201